Community Detection and Evaluation in Bipartite Networks

• IKEYA Tomoyuki

  Dept. of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology

• MURATA Tsuyoshi

  Dept. of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology

Bibliographic Information

Other Title

• 2部ネットワークにおけるコミュニティ検出とその評価手法

Abstract

Many real-world data can be represented as bipartite networks composed of two types of vertices. Paperauthor networks and event-attendee networks are the examples of bipartite networks. Detecting communities from such bipartite networks is practically important for finding similar items and for understanding the structures of the networks. In order to evaluate the goodness of detected communities from unipartite networks, Newman-Girvan modularity is often employed. For bipartite networks, Barber, Guimera, Murata and Suzuki propose bipartite modularities. This paper compares these bipartite modularities in order to understand their properties. Experimental results for synthetic bipartite networks show that (1) computation of Barber bipartite modularity is relatively fast, and (2) accuracies of communities detected by maximizing Suzuki bipartite modularity is relatively better than maximizing other bipartite modularities. In addition, we implemented a fast optimization method for detecting communities in large bipartite networks.

Journal

• Computer Software

  Computer Software 28 (1), 91-102, 2011

  Japan Society for Software Science and Technology

Details

• CRID: 1390001204738405120
• NII Article ID: 130004892169
• DOI: 10.11309/jssst.28.1_91
• ISSN: 02896540
• Data Source:
  • JaLC
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed